Ryan McKinley wrote:

True, although function query tends to bring more overhead ...

While we're on the subject of complex scoring - I read an interesting paper (I don't have a link now), which discussed a so called bucketed scoring. The idea is that if your basic scoring is good enough to ensure that top-N results are highly relevant, then you can split these results into buckets of k documents (let's say 10 ;) ), and within each bucket apply arbitrary re-ranking function, which is then very inexpensive to perform because of the limited number of documents.

Example: you have a large corpus of web pages, and you want home pages to appear first, even if they score somewhat lower - and it doesn't pay off to modify the base scoring, because of overfitting, i.e. the scoring would be good for home pages but poor for other relevant documents.